cs249r_book/tinytorch/tests

TinyTorch Test Suite

Comprehensive testing organized by purpose and scope.

Test Organization

📦 Module Tests (XX_modulename/)

Purpose: Test individual module functionality
Scope: Single module, isolated behavior
Example: 01_tensor/test_progressive_integration.py

These tests validate that each module works correctly in isolation.

🔗 Integration Tests (integration/)

Purpose: Test cross-module interactions
Scope: Multiple modules working together
Files:

• test_gradient_flow.py - CRITICAL: Validates gradients flow through entire training stack
• test_end_to_end_training.py - Full training loops (TODO)
• test_module_compatibility.py - Module interfaces (TODO)

Why this matters:

• Catches bugs that unit tests miss
• Validates the "seams" between modules
• Ensures training actually works end-to-end

🐛 Debugging Tests (debugging/)

Purpose: Catch common student pitfalls
Scope: Pedagogical - teaches debugging
Files:

• test_gradient_vanishing.py - Detect/diagnose vanishing gradients (TODO)
• test_gradient_explosion.py - Detect/diagnose exploding gradients (TODO)
• test_common_mistakes.py - "Did you forget backward()?" style tests (TODO)

Philosophy: When these tests fail, the error message should teach the student what went wrong and how to fix it.

⚡ Autograd Edge Cases (05_autograd/)

Purpose: Stress-test autograd system
Scope: Autograd internals and edge cases
Files:

• test_broadcasting.py - Broadcasting gradient bugs (TODO)
• test_computation_graph.py - Graph construction edge cases (TODO)
• test_backward_edge_cases.py - Numerical stability, etc. (TODO)

Running Tests

Standard Mode

pytest tests/ -v                    # All tests
pytest tests/integration/ -v        # Integration tests only
pytest tests/01_tensor/ -v          # Specific module

🎓 Educational Mode (Recommended for Students)

pytest tests/ --tinytorch           # Rich output with WHAT/WHY context
pytest tests/01_tensor/ --tinytorch # Single module with education

Educational mode shows:

• Module groupings before running
• What each test does (WHAT)
• Why it matters (WHY)
• Learning tips on failure (STUDENT LEARNING)
• Clear pass/fail indicators with Rich formatting

Run without pytest

python tests/integration/test_gradient_flow.py

Test Philosophy

1. Integration tests catch real bugs: The gradient flow test caught the exact bugs that prevented training
2. Descriptive names: Test names should explain what they test
3. Good error messages: When tests fail, students should understand why
4. Pedagogical value: Tests teach correct usage patterns

Educational Test Docstrings

All *_core.py test files use a structured docstring format:

def test_tensor_addition(self):
    """
    WHAT: Element-wise tensor addition.
    
    WHY: Addition is used everywhere in neural networks:
    - Adding bias to layer output: y = Wx + b
    - Residual connections: output = layer(x) + x
    
    STUDENT LEARNING: Operations return new Tensors (functional style).
    """

This format enables the --tinytorch flag to show educational context when tests run.

Adding New Tests

When adding a test, ask:

• Is it testing one module? → Put in XX_modulename/
• Is it testing modules working together? → Put in integration/
• Is it teaching debugging? → Put in debugging/
• Is it an autograd edge case? → Put in 05_autograd/

Most Important Tests

🔥 Must pass before merging:

• integration/test_gradient_flow.py - If this fails, training is broken

📚 Module validation:

• Each module's inline tests (in modules/)
• Module-specific tests in tests/XX_modulename/

Test Coverage Goals

• ✅ All tensor operations have gradient tests
• ✅ All layers compute gradients correctly
• ✅ All activations integrate with autograd
• ✅ All loss functions compute gradients
• ✅ All optimizers update parameters
• ⏳ End-to-end training converges (TODO)
• ⏳ Common pitfalls are detected (TODO)